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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

  • Jincai Huang
  • , Yongjun Xu
  • , Qi Wang
  • , Qi (Cheems) Wang
  • , Xingxing Liang
  • , Fei Wang
  • , Zhao Zhang
  • , Wei Wei
  • , Boxuan Zhang
  • , Libo Huang
  • , Jingru Chang
  • , Liantao Ma
  • , Ting Ma
  • , Yuxuan Liang
  • , Jie Zhang
  • , Jian Guo
  • , Xuhui Jiang
  • , Xinxin Fan
  • , Zhulin An
  • , Tingting Li
  • Xuefei Li, Zezhi Shao, Tangwen Qian, Tao Sun, Boyu Diao, Chuanguang Yang, Chenqing Yu, Yiqing Wu, Mengxian Li, Haifeng Zhang, Yongcheng Zeng, Zhicheng Zhang, Zhengqiu Zhu, Yiqin Lv, Aming Li, Xu Chen, Bo An, Wei Xiao, Chenguang Bai, Yuxing Mao, Zhigang Yin, Sheng Gui, Wentao Su, Yinghao Zhu, Junyi Gao, Xinyu He, Yizhou Li, Guangyin Jin, Xiang Ao, Xuehao Zhai, Haoran Tan, Lijun Yun, Hongquan Shi, Jun Li, Changjun Fan, Kuihua Huang, Ewen Harrison, Victor C.M. Leung, Sihang Qiu*, Yanjie Dong*, Xiaolong Zheng*, Gang Wang*, Yu Zheng*, Yuanzhuo Wang*, Jiafeng Guo*, Lizhe Wang*, Xueqi Cheng*, Yaonan Wang*, Shanlin Yang*, Mengyin Fu*, Aiguo Fei*
*此作品的通讯作者
  • National University of Defense Technology
  • CAS - Institute of Computing Technology
  • University of Chinese Academy of Sciences
  • State Key Laboratory of AI Safety
  • Tsinghua University
  • Huazhong University of Science and Technology
  • Beijing Institute of Technology
  • Dalian Polytechnic University
  • Peking University
  • The Hong Kong University of Science and Technology (Guangzhou)
  • China Agricultural University
  • State Key Laboratory of Efficient Utilization of Agricultural Water Resources
  • International Digital Economy Academy
  • CAS - Institute of Automation
  • Gaoling School of Artificial Intelligence
  • Nanyang Technological University
  • Chongqing University
  • The University of Hong Kong
  • CAS - Academy of Mathematics and System Sciences
  • University of Edinburgh
  • Health Data Research UK
  • Chinese Academy of Medical Sciences
  • National Innovation Institute of Defense Technology
  • Imperial College London
  • Hunan University
  • Ltd.
  • Dalian Naval Academy
  • China University of Geosciences, Wuhan
  • Shenzhen MSU-BIT University
  • Shenzhen University
  • University of British Columbia
  • JD Intelligent Cities Business Unit
  • Hefei University of Technology
  • Nanjing University of Science and Technology
  • Beijing University of Posts and Telecommunications

科研成果: 期刊稿件文献综述同行评审

摘要

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.

源语言英语
文章编号100948
期刊Innovation
6
6
DOI
出版状态已出版 - 2 6月 2025
已对外发布

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